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Can the NVIDIA B200 run MiniMax-M1 80K?

58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (offloads to RAM)

Runs well (offloads to RAM). At Q2_K it needs about 192 GB of 180 GB usable, with an estimated speed of 34.6 tok/s.

Best quant
Q2_K
Generation speed
34.6 tok/s
Prompt processing
2,391 tok/s
First token (1K prompt)
0.4 s
Largest safe context
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Best app to run it on the NVIDIA B200

On Linux

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Handy if you already work in Docker.
docker model run hf.co/MiniMaxAI/MiniMax-M1-80k

On Windows

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Handy if you already work in Docker.
docker model run hf.co/MiniMaxAI/MiniMax-M1-80k

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download MiniMax-M1 80K

Original weights · Hugging Face

Full-precision files from MiniMax, for fine-tuning, vLLM, Transformers.

huggingface.co/MiniMaxAI/MiniMax-M1-80k (opens in a new tab)

No sign-in needed.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs welloffloads to RAM
How well it runs on the NVIDIA B200: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.26.1 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.13.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score65.1 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the NVIDIA B200

Quant Memory needed Available Speed Runs Quality Download
Q2_K 192 GB 180 GB 34.6 tok/s Runs welloffloads to RAM 58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 233 GB 180 GB — Won't run 69Good quality (B) · 69 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 288 GB 180 GB — Won't run 78Great quality (A) · 78 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 334 GB 180 GB — Won't run 80Great quality (A) · 80 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 384 GB 180 GB — Won't run 82Great quality (A) · 82 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 493 GB 180 GB — Won't run 83Great quality (A) · 83 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 922 GB 180 GB — Won't run 83Great quality (A) · 83 of 100: how capable the model is at F16, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does MiniMax-M1 80K need on the NVIDIA B200 180GB?

At Q2_K it needs about 192 GB. The NVIDIA B200 180GB has 180 GB usable.

How fast is MiniMax-M1 80K on the NVIDIA B200 180GB?

About 34.6 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of MiniMax-M1 80K should I use on the NVIDIA B200 180GB?

Q2_K. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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